Table operations

In this section we describe higher-level operations that can be used to generate a new table from one or more input tables. This includes:


Documentation Description Function
Grouped operations Group tables and columns by keys group_by
Binning Binning tables group_by
Stack vertically Concatenate input tables along rows vstack
Stack horizontally Concatenate input tables along columns hstack
Join Database-style join of two tables join
Unique rows Unique table rows by keys unique

Grouped operations

Sometimes in a table or table column there are natural groups within the dataset for which it makes sense to compute some derived values. A simple example is a list of objects with photometry from various observing runs:

>>> from astropy.table import Table
>>> obs = Table.read("""name    obs_date    mag_b  mag_v
...                     M31     2012-01-02  17.0   17.5
...                     M31     2012-01-02  17.1   17.4
...                     M101    2012-01-02  15.1   13.5
...                     M82     2012-02-14  16.2   14.5
...                     M31     2012-02-14  16.9   17.3
...                     M82     2012-02-14  15.2   15.5
...                     M101    2012-02-14  15.0   13.6
...                     M82     2012-03-26  15.7   16.5
...                     M101    2012-03-26  15.1   13.5
...                     M101    2012-03-26  14.8   14.3
...                     """, format='ascii')

Table groups

Now suppose we want the mean magnitudes for each object. We first group the data by the name column with the group_by() method. This returns a new table sorted by name which has a groups property specifying the unique values of name and the corresponding table rows:

>>> obs_by_name = obs.group_by('name')
>>> print(obs_by_name)  
name  obs_date  mag_b mag_v
---- ---------- ----- -----
M101 2012-01-02  15.1  13.5  << First group (index=0, key='M101')
M101 2012-02-14  15.0  13.6
M101 2012-03-26  15.1  13.5
M101 2012-03-26  14.8  14.3
 M31 2012-01-02  17.0  17.5  << Second group (index=4, key='M31')
 M31 2012-01-02  17.1  17.4
 M31 2012-02-14  16.9  17.3
 M82 2012-02-14  16.2  14.5  << Third group (index=7, key='M83')
 M82 2012-02-14  15.2  15.5
 M82 2012-03-26  15.7  16.5
                             << End of groups (index=10)
>>> print(obs_by_name.groups.keys)
name
----
M101
 M31
 M82
>>> print(obs_by_name.groups.indices)
[ 0  4  7 10]

The groups property is the portal to all grouped operations with tables and columns. It defines how the table is grouped via an array of the unique row key values and the indices of the group boundaries for those key values. The groups here correspond to the row slices 0:4, 4:7, and 7:10 in the obs_by_name table.

The initial argument (keys) for the group_by function can take a number of input data types:

  • Single string value with a table column name (as shown above)
  • List of string values with table column names
  • Another Table or Column with same length as table
  • Numpy structured array with same length as table
  • Numpy homogeneous array with same length as table

In all cases the corresponding row elements are considered as a tuple of values which form a key value that is used to sort the original table and generate the required groups.

As an example, to get the average magnitudes for each object on each observing night, we would first group the table on both name and obs_date as follows:

>>> print(obs.group_by(['name', 'obs_date']).groups.keys)
name  obs_date
---- ----------
M101 2012-01-02
M101 2012-02-14
M101 2012-03-26
 M31 2012-01-02
 M31 2012-02-14
 M82 2012-02-14
 M82 2012-03-26

Manipulating groups

Once you have applied grouping to a table then you can easily access the individual groups or subsets of groups. In all cases this returns a new grouped table. For instance to get the sub-table which corresponds to the second group (index=1) do:

>>> print(obs_by_name.groups[1])
name  obs_date  mag_b mag_v
---- ---------- ----- -----
 M31 2012-01-02  17.0  17.5
 M31 2012-01-02  17.1  17.4
 M31 2012-02-14  16.9  17.3

To get the first and second groups together use a slice:

>>> groups01 = obs_by_name.groups[0:2]
>>> print(groups01)
name  obs_date  mag_b mag_v
---- ---------- ----- -----
M101 2012-01-02  15.1  13.5
M101 2012-02-14  15.0  13.6
M101 2012-03-26  15.1  13.5
M101 2012-03-26  14.8  14.3
 M31 2012-01-02  17.0  17.5
 M31 2012-01-02  17.1  17.4
 M31 2012-02-14  16.9  17.3
>>> print(groups01.groups.keys)
name
----
M101
 M31

You can also supply a numpy array of indices or a boolean mask to select particular groups, e.g.:

>>> mask = obs_by_name.groups.keys['name'] == 'M101'
>>> print(obs_by_name.groups[mask])
name  obs_date  mag_b mag_v
---- ---------- ----- -----
M101 2012-01-02  15.1  13.5
M101 2012-02-14  15.0  13.6
M101 2012-03-26  15.1  13.5
M101 2012-03-26  14.8  14.3

One can iterate over the group sub-tables and corresponding keys with:

>>> for key, group in zip(obs_by_name.groups.keys, obs_by_name.groups):
...     print('****** {0} *******'.format(key['name']))
...     print(group)
...     print('')
...
****** M101 *******
name  obs_date  mag_b mag_v
---- ---------- ----- -----
M101 2012-01-02  15.1  13.5
M101 2012-02-14  15.0  13.6
M101 2012-03-26  15.1  13.5
M101 2012-03-26  14.8  14.3
****** M31 *******
name  obs_date  mag_b mag_v
---- ---------- ----- -----
 M31 2012-01-02  17.0  17.5
 M31 2012-01-02  17.1  17.4
 M31 2012-02-14  16.9  17.3
****** M82 *******
name  obs_date  mag_b mag_v
---- ---------- ----- -----
 M82 2012-02-14  16.2  14.5
 M82 2012-02-14  15.2  15.5
 M82 2012-03-26  15.7  16.5

Column Groups

Like Table objects, Column objects can also be grouped for subsequent manipulation with grouped operations. This can apply both to columns within a Table or bare Column objects.

As for Table, the grouping is generated with the group_by method. The difference here is that there is no option of providing one or more column names since that doesn’t make sense for a Column.

Examples:

>>> from astropy.table import Column
>>> import numpy as np
>>> c = Column([1, 2, 3, 4, 5, 6], name='a')
>>> key_vals = np.array(['foo', 'bar', 'foo', 'foo', 'qux', 'qux'])
>>> cg = c.group_by(key_vals)

>>> for key, group in zip(cg.groups.keys, cg.groups):
...     print('****** {0} *******'.format(key))
...     print(group)
...     print('')
...
****** bar *******
 a
---
  2
****** foo *******
 a
---
  1
  3
  4
****** qux *******
 a
---
  5
  6

Aggregation

Aggregation is the process of applying a specified reduction function to the values within each group for each non-key column. This function must accept a numpy array as the first argument and return a single scalar value. Common function examples are numpy.sum, numpy.mean, and numpy.std.

For the example grouped table obs_by_name from above we compute the group means with the aggregate method:

>>> obs_mean = obs_by_name.groups.aggregate(np.mean)  
WARNING: Cannot aggregate column 'obs_date' [astropy.table.groups]
>>> print(obs_mean)  
name mag_b mag_v
---- ----- ------
M101  15.0 13.725
 M31  17.0   17.4
 M82  15.7   15.5

It seems the magnitude values were successfully averaged, but what about the WARNING? Since the obs_date column is a string-type array, the numpy.mean function failed and raised an exception. Any time this happens then aggregate will issue a warning and then drop that column from the output result. Note that the name column is one of the keys used to determine the grouping so it is automatically ignored from aggregation.

From a grouped table it is possible to select one or more columns on which to perform the aggregation:

>>> print(obs_by_name['mag_b'].groups.aggregate(np.mean))
mag_b
-----
 15.0
 17.0
 15.7

>>> print(obs_by_name['name', 'mag_v', 'mag_b'].groups.aggregate(np.mean))
name mag_v  mag_b
---- ------ -----
M101 13.725  15.0
 M31   17.4  17.0
 M82   15.5  15.7

A single column of data can be aggregated as well:

>>> c = Column([1, 2, 3, 4, 5, 6], name='a')
>>> key_vals = np.array(['foo', 'bar', 'foo', 'foo', 'qux', 'qux'])
>>> cg = c.group_by(key_vals)
>>> cg_sums = cg.groups.aggregate(np.sum)
>>> for key, cg_sum in zip(cg.groups.keys, cg_sums):
...     print('Sum for {0} = {1}'.format(key, cg_sum))
...
Sum for bar = 2
Sum for foo = 8
Sum for qux = 11

If the specified function has a numpy.ufunc.reduceat method, this will be called instead. This can improve the performance by a factor of 10 to 100 (or more) for large unmasked tables or columns with many relatively small groups. It also allows for the use of certain numpy functions which normally take more than one input array but also work as reduction functions, like numpy.add. The numpy functions which should take advantage of using numpy.ufunc.reduceat include:

numpy.add, numpy.arctan2, numpy.bitwise_and, numpy.bitwise_or, numpy.bitwise_xor, numpy.copysign, numpy.divide, numpy.equal, numpy.floor_divide, numpy.fmax, numpy.fmin, numpy.fmod, numpy.greater_equal, numpy.greater, numpy.hypot, numpy.left_shift, numpy.less_equal, numpy.less, numpy.logaddexp2, numpy.logaddexp, numpy.logical_and, numpy.logical_or, numpy.logical_xor, numpy.maximum, numpy.minimum, numpy.mod, numpy.multiply, numpy.not_equal, numpy.power, numpy.remainder, numpy.right_shift, numpy.subtract and numpy.true_divide.

As special cases numpy.sum and numpy.mean are substituted with their respective reduceat methods.

Filtering

Table groups can be filtered by means of the filter method. This is done by supplying a function which is called for each group. The function which is passed to this method must accept two arguments:

  • table : Table object
  • key_colnames : list of columns in table used as keys for grouping

It must then return either True or False. As an example, the following will select all table groups with only positive values in the non-key columns:

>>> def all_positive(table, key_colnames):
...     colnames = [name for name in table.colnames if name not in key_colnames]
...     for colname in colnames:
...         if np.any(table[colname] < 0):
...             return False
...     return True

An example of using this function is:

>>> t = Table.read(""" a   b    c
...                   -2  7.0   0
...                   -2  5.0   1
...                    1  3.0  -5
...                    1 -2.0  -6
...                    1  1.0   7
...                    0  0.0   4
...                    3  3.0   5
...                    3 -2.0   6
...                    3  1.0   7""", format='ascii')
>>> tg = t.group_by('a')
>>> t_positive = tg.groups.filter(all_positive)
>>> for group in t_positive.groups:
...     print(group)
...     print('')
...
 a   b   c
--- --- ---
 -2 7.0   0
 -2 5.0   1

 a   b   c
--- --- ---
  0 0.0   4

As can be seen only the groups with a == -2 and a == 0 have all positive values in the non-key columns, so those are the ones that are selected.

Likewise a grouped column can be filtered with the filter, method but in this case the filtering function takes only a single argument which is the column group. It still must return either True or False. For example:

def all_positive(column):
    if np.any(column < 0):
        return False
    return True

Binning

A common tool in analysis is to bin a table based on some reference value. Examples:

  • Photometry of a binary star in several bands taken over a span of time which should be binned by orbital phase.
  • Reducing the sampling density for a table by combining 100 rows at a time.
  • Unevenly sampled historical data which should binned to four points per year.

All of these examples of binning a table can be easily accomplished using grouped operations. The examples in that section are focused on the case of discrete key values such as the name of a source. In this section we show a simple yet powerful way of applying grouped operations to accomplish binning on key values such as time, phase or row number.

The common theme in all these cases is to convert the key value array into a new float- or int-valued array whose values are identical for rows in the same output bin. As an example, generate a fake light curve:

>>> year = np.linspace(2000.0, 2010.0, 200)  # 200 observations over 10 years
>>> period = 1.811
>>> y0 = 2005.2
>>> mag = 14.0 + 1.2 * np.sin(2 * np.pi * (year - y0) / period)
>>> phase = ((year - y0) / period) % 1.0
>>> dat = Table([year, phase, mag], names=['year', 'phase', 'mag'])

Now make an array that will be used for binning the data by 0.25 year intervals:

>>> year_bin = np.trunc(year / 0.25)

This has the property that all samples in each 0.25 year bin have the same value of year_bin. Think of year_bin as the bin number for year. Then do the binning by grouping and immediately aggregating with np.mean.

>>> dat_grouped = dat.group_by(year_bin)
>>> dat_binned = dat_grouped.groups.aggregate(np.mean)

Then one might plot the results with plt.plot(dat_binned['year'], dat_binned['mag'], '.'). Alternately one could bin into 10 phase bins:

>>> phase_bin = np.trunc(phase / 0.1)
>>> dat_grouped = dat.group_by(phase_bin)
>>> dat_binned = dat_grouped.groups.aggregate(np.mean)

This time plot with plt.plot(dat_binned['phase'], dat_binned['mag']).

Stack vertically

The Table class supports stacking tables vertically with the vstack function. This process is also commonly known as concatenating or appending tables in the row direction. It corresponds roughly to the numpy.vstack function.

For example, suppose one has two tables of observations with several column names in common:

>>> from astropy.table import Table, vstack
>>> obs1 = Table.read("""name    obs_date    mag_b  logLx
...                      M31     2012-01-02  17.0   42.5
...                      M82     2012-10-29  16.2   43.5
...                      M101    2012-10-31  15.1   44.5""", format='ascii')

>>> obs2 = Table.read("""name    obs_date    logLx
...                      NGC3516 2011-11-11  42.1
...                      M31     1999-01-05  43.1
...                      M82     2012-10-30  45.0""", format='ascii')

Now we can stack these two tables:

>>> print(vstack([obs1, obs2]))
  name   obs_date  mag_b logLx
------- ---------- ----- -----
    M31 2012-01-02  17.0  42.5
    M82 2012-10-29  16.2  43.5
   M101 2012-10-31  15.1  44.5
NGC3516 2011-11-11    --  42.1
    M31 1999-01-05    --  43.1
    M82 2012-10-30    --  45.0

Notice that the obs2 table is missing the mag_b column, so in the stacked output table those values are marked as missing. This is the default behavior and corresponds to join_type='outer'. There are two other allowed values for the join_type argument, 'inner' and 'exact':

>>> print(vstack([obs1, obs2], join_type='inner'))
  name   obs_date  logLx
------- ---------- -----
    M31 2012-01-02  42.5
    M82 2012-10-29  43.5
   M101 2012-10-31  44.5
NGC3516 2011-11-11  42.1
    M31 1999-01-05  43.1
    M82 2012-10-30  45.0

>>> print(vstack([obs1, obs2], join_type='exact'))  
Traceback (most recent call last):
  ...
TableMergeError: Inconsistent columns in input arrays (use 'inner'
or 'outer' join_type to allow non-matching columns)

In the case of join_type='inner', only the common columns (the intersection) are present in the output table. When join_type='exact' is specified then vstack requires that all the input tables have exactly the same column names.

More than two tables can be stacked by supplying a list of table objects:

>>> obs3 = Table.read("""name    obs_date    mag_b  logLx
...                      M45     2012-02-03  15.0   40.5""", format='ascii')
>>> print(vstack([obs1, obs2, obs3]))
  name   obs_date  mag_b logLx
------- ---------- ----- -----
    M31 2012-01-02  17.0  42.5
    M82 2012-10-29  16.2  43.5
   M101 2012-10-31  15.1  44.5
NGC3516 2011-11-11    --  42.1
    M31 1999-01-05    --  43.1
    M82 2012-10-30    --  45.0
    M45 2012-02-03  15.0  40.5

See also the sections on Merging metadata and Merging column attributes for details on how these characteristics of the input tables are merged in the single output table. Note also that you can use a single table row instead of a full table as one of the inputs.

Stack horizontally

The Table class supports stacking tables horizontally (in the column-wise direction) with the hstack function. It corresponds roughly to the numpy.hstack function.

For example, suppose one has the following two tables:

>>> from astropy.table import Table, hstack
>>> t1 = Table.read("""a   b    c
...                    1   foo  1.4
...                    2   bar  2.1
...                    3   baz  2.8""", format='ascii')
>>> t2 = Table.read("""d     e
...                    ham   eggs
...                    spam  toast""", format='ascii')

Now we can stack these two tables horizontally:

>>> print(hstack([t1, t2]))
 a   b   c   d     e
--- --- --- ---- -----
  1 foo 1.4  ham  eggs
  2 bar 2.1 spam toast
  3 baz 2.8   --    --

As with vstack, there is an optional join_type argument that can take values 'inner', 'exact', and 'outer'. The default is 'outer', which effectively takes the union of available rows and masks out any missing values. This is illustrated in the example above. The other options give the intersection of rows, where 'exact' requires that all tables have exactly the same number of rows:

>>> print(hstack([t1, t2], join_type='inner'))
 a   b   c   d     e
--- --- --- ---- -----
  1 foo 1.4  ham  eggs
  2 bar 2.1 spam toast

>>> print(hstack([t1, t2], join_type='exact'))  
Traceback (most recent call last):
  ...
TableMergeError: Inconsistent number of rows in input arrays (use 'inner' or
'outer' join_type to allow non-matching rows)

More than two tables can be stacked by supplying a list of table objects. The example below also illustrates the behavior when there is a conflict in the input column names (see the section on Column renaming for details):

>>> t3 = Table.read("""a    b
...                    M45  2012-02-03""", format='ascii')
>>> print(hstack([t1, t2, t3]))
a_1 b_1  c   d     e   a_3    b_3
--- --- --- ---- ----- --- ----------
  1 foo 1.4  ham  eggs M45 2012-02-03
  2 bar 2.1 spam toast  --         --
  3 baz 2.8   --    --  --         --

The metadata from the input tables is merged by the process described in the Merging metadata section. Note also that you can use a single table row instead of a full table as one of the inputs.

Join

The Table class supports the database join operation. This provides a flexible and powerful way to combine tables based on the values in one or more key columns.

For example, suppose one has two tables of observations, the first with B and V magnitudes and the second with X-ray luminosities of an overlapping (but not identical) sample:

>>> from astropy.table import Table, join
>>> optical = Table.read("""name    obs_date    mag_b  mag_v
...                         M31     2012-01-02  17.0   16.0
...                         M82     2012-10-29  16.2   15.2
...                         M101    2012-10-31  15.1   15.5""", format='ascii')
>>> xray = Table.read("""   name    obs_date    logLx
...                         NGC3516 2011-11-11  42.1
...                         M31     1999-01-05  43.1
...                         M82     2012-10-29  45.0""", format='ascii')

The join() method allows one to merge these two tables into a single table based on matching values in the “key columns”. By default the key columns are the set of columns that are common to both tables. In this case the key columns are name and obs_date. We can find all the observations of the same object on the same date as follows:

>>> opt_xray = join(optical, xray)
>>> print(opt_xray)
name  obs_date  mag_b mag_v logLx
---- ---------- ----- ----- -----
 M82 2012-10-29  16.2  15.2  45.0

We can perform the match only by name by providing the keys argument, which can be either a single column name or a list of column names:

>>> print(join(optical, xray, keys='name'))
name obs_date_1 mag_b mag_v obs_date_2 logLx
---- ---------- ----- ----- ---------- -----
 M31 2012-01-02  17.0  16.0 1999-01-05  43.1
 M82 2012-10-29  16.2  15.2 2012-10-29  45.0

This output table has all observations that have both optical and X-ray data for an object (M31 and M82). Notice that since the obs_date column occurs in both tables it has been split into two columns, obs_date_1 and obs_date_2. The values are taken from the “left” (optical) and “right” (xray) tables, respectively.

Different join options

The table joins so far are known as “inner” joins and represent the strict intersection of the two tables on the key columns.

If one wants to make a new table which has every row from the left table and includes matching values from the right table when available, this is known as a left join:

>>> print(join(optical, xray, join_type='left'))
name  obs_date  mag_b mag_v logLx
---- ---------- ----- ----- -----
M101 2012-10-31  15.1  15.5    --
 M31 2012-01-02  17.0  16.0    --
 M82 2012-10-29  16.2  15.2  45.0

Two of the observations do not have X-ray data, as indicated by the -- in the table. When there are any missing values the output will be a masked table (see Masking and missing values for more information). You might be surprised that there is no X-ray data for M31 in the output. Remember that the default matching key includes both name and obs_date. Specifying the key as only the name column gives:

>>> print(join(optical, xray, join_type='left', keys='name'))
name obs_date_1 mag_b mag_v obs_date_2 logLx
---- ---------- ----- ----- ---------- -----
M101 2012-10-31  15.1  15.5         --    --
 M31 2012-01-02  17.0  16.0 1999-01-05  43.1
 M82 2012-10-29  16.2  15.2 2012-10-29  45.0

Likewise one can construct a new table with every row of the right table and matching left values (when available) using join_type='right'.

Finally, to make a table with the union of rows from both tables do an “outer” join:

>>> print(join(optical, xray, join_type='outer'))
  name   obs_date  mag_b mag_v logLx
------- ---------- ----- ----- -----
   M101 2012-10-31  15.1  15.5    --
    M31 1999-01-05    --    --  43.1
    M31 2012-01-02  17.0  16.0    --
    M82 2012-10-29  16.2  15.2  45.0
NGC3516 2011-11-11    --    --  42.1

Non-identical key column names

The join() function requires the key column names to be identical in the two tables. However, in the following one table has a 'name' column while the other has an 'obj_id' column:

>>> optical = Table.read("""name    obs_date    mag_b  mag_v
...                         M31     2012-01-02  17.0   16.0
...                         M82     2012-10-29  16.2   15.2
...                         M101    2012-10-31  15.1   15.5""", format='ascii')
>>> xray_1 = Table.read("""   obj_id    obs_date    logLx
...                           NGC3516 2011-11-11  42.1
...                           M31     1999-01-05  43.1
...                           M82     2012-10-29  45.0""", format='ascii')

In order to perform a match based on the names of the objects, one has to temporarily rename one of the columns mentioned above, right before creating the new table:

>>> xray_1.rename_column('obj_id', 'name')
>>> opt_xray_1 = join(optical, xray_1, keys='name')
>>> xray_1.rename_column('name', 'obj_id')
>>> print(opt_xray_1)
name obs_date_1 mag_b mag_v obs_date_2 logLx
---- ---------- ----- ----- ---------- -----
M31 2012-01-02  17.0  16.0 1999-01-05  43.1
M82 2012-10-29  16.2  15.2 2012-10-29  45.0

The original xray_1 table remains unchanged after the operation:

>>> print(xray_1)
obj_id  obs_date  logLx
------- ---------- -----
NGC3516 2011-11-11  42.1
    M31 1999-01-05  43.1
    M82 2012-10-29  45.0

Identical key values

The Table join operation works even if there are multiple rows with identical key values. For example the following tables have multiple rows for the key column x:

>>> from astropy.table import Table, join
>>> left = Table([[0, 1, 1, 2], ['L1', 'L2', 'L3', 'L4']], names=('key', 'L'))
>>> right = Table([[1, 1, 2, 4], ['R1', 'R2', 'R3', 'R4']], names=('key', 'R'))
>>> print(left)
key  L
--- ---
  0  L1
  1  L2
  1  L3
  2  L4
>>> print(right)
key  R
--- ---
  1  R1
  1  R2
  2  R3
  4  R4

Doing an outer join on these tables shows that what is really happening is a Cartesian product. For each matching key, every combination of the left and right tables is represented. When there is no match in either the left or right table, the corresponding column values are designated as missing.

>>> print(join(left, right, join_type='outer'))
key  L   R
--- --- ---
  0  L1  --
  1  L2  R1
  1  L2  R2
  1  L3  R1
  1  L3  R2
  2  L4  R3
  4  --  R4

Note

The output table is sorted on the key columns, but when there are rows with identical keys the output order in the non-key columns is not guaranteed to be identical across installations. In the example above the order within the four rows with key == 1 can vary.

An inner join is the same but only returns rows where there is a key match in both the left and right tables:

>>> print(join(left, right, join_type='inner'))
key  L   R
--- --- ---
  1  L2  R1
  1  L2  R2
  1  L3  R1
  1  L3  R2
  2  L4  R3

Conflicts in the input table names are handled by the process described in the section on Column renaming. See also the sections on Merging metadata and Merging column attributes for details on how these characteristics of the input tables are merged in the single output table.

Merging details

When combining two or more tables there is the need to merge certain characteristics in the inputs and potentially resolve conflicts. This section describes the process.

Column renaming

In cases where the input tables have conflicting column names, there is a mechanism to generate unique output column names. There are two keyword arguments that control the renaming behavior:

table_names
Two-element list of strings that provide a name for the tables being joined. By default this is ['1', '2', ...], where the numbers correspond to the input tables.
uniq_col_name
String format specifier with a default value of '{col_name}_{table_name}'.

This is most easily understood by example using the optical and xray tables in the join() example defined previously:

>>> print(join(optical, xray, keys='name',
...            table_names=['OPTICAL', 'XRAY'],
...            uniq_col_name='{table_name}_{col_name}'))
name OPTICAL_obs_date mag_b mag_v XRAY_obs_date logLx
---- ---------------- ----- ----- ------------- -----
 M31       2012-01-02  17.0  16.0    1999-01-05  43.1
 M82       2012-10-29  16.2  15.2    2012-10-29  45.0

Merging metadata

Table objects can have associated metadata:

  • Table.meta: table-level metadata as an ordered dictionary
  • Column.meta: per-column metadata as an ordered dictionary

The table operations described here handle the task of merging the metadata in the input tables into a single output structure. Because the metadata can be arbitrarily complex there is no unique way to do the merge. The current implementation uses a simple recursive algorithm with four rules:

  • dict elements are merged by keys
  • Conflicting list or tuple elements are concatenated
  • Conflicting dict elements are merged by recursively calling the merge function
  • Conflicting elements that are not both list, tuple, or dict will follow the following rules:
    • If both metadata values are identical, the output is set to this value
    • If one of the conflicting metadata values is None, the other value is picked
    • If both metadata values are different and neither is None, the one for the last table in the list is picked

By default, a warning is emitted in the last case (both metadata values are not None). The warning can be silenced or made into an exception using the metadata_conflicts argument to hstack(), vstack(), or join(). The metadata_conflicts option can be set to:

  • 'silent' - no warning is emitted, the value for the last table is silently picked
  • 'warn' - a warning is emitted, the value for the last table is picked
  • 'error' - an exception is raised

The default strategies for merging metadata can be augmented or customized by defining subclasses of the MergeStrategy base class. In most cases one also will use the enable_merge_strategies for enable the custom strategies. The linked documentation strings provide details.

Merging column attributes

In addition to the table and column meta attributes, the column attributes unit, format, and description are merged by going through the input tables in order and taking the first value which is defined (i.e. is not None). For example:

>>> from astropy.table import Column, Table, vstack
>>> col1 = Column([1], name='a')
>>> col2 = Column([2], name='a', unit='cm')
>>> col3 = Column([3], name='a', unit='m')
>>> t1 = Table([col1])
>>> t2 = Table([col2])
>>> t3 = Table([col3])
>>> out = vstack([t1, t2, t3])  
WARNING: MergeConflictWarning: In merged column 'a' the 'unit' attribute does
not match (cm != m).  Using m for merged output [astropy.table.operations]
>>> out['a'].unit  
Unit("m")

The rules for merging are as for Merging metadata, and the metadata_conflicts option also controls the merging of column attributes.

Unique rows

Sometimes it makes sense to use only rows with unique key columns or even fully unique rows from a table. This can be done using the above described group_by() method and groups attribute, or with the unique convenience function. The unique function returns with a sorted table containing the first row for each unique keys column value. If no keys is provided it returns with a sorted table containing all the fully unique rows.

A simple example is a list of objects with photometry from various observing runs. Using 'name' as the only keys, it returns with the first occurrence of each of the three targets:

>>> from astropy import table
>>> obs = table.Table.read("""name    obs_date    mag_b  mag_v
...                           M31     2012-01-02  17.0   17.5
...                           M82     2012-02-14  16.2   14.5
...                           M101    2012-01-02  15.1   13.5
...                           M31     2012-01-02  17.1   17.4
...                           M101    2012-01-02  15.1   13.5
...                           M82     2012-02-14  16.2   14.5
...                           M31     2012-02-14  16.9   17.3
...                           M82     2012-02-14  15.2   15.5
...                           M101    2012-02-14  15.0   13.6
...                           M82     2012-03-26  15.7   16.5
...                           M101    2012-03-26  15.1   13.5
...                           M101    2012-03-26  14.8   14.3
...                           """, format='ascii')
>>> unique_by_name = table.unique(obs, keys='name')
>>> print(unique_by_name)
name  obs_date  mag_b mag_v
---- ---------- ----- -----
M101 2012-01-02  15.1  13.5
 M31 2012-01-02  17.0  17.5
 M82 2012-02-14  16.2  14.5

Using multiple columns as keys:

>>> unique_by_name_date = table.unique(obs, keys=['name', 'obs_date'])
>>> print(unique_by_name_date)
name  obs_date  mag_b mag_v
---- ---------- ----- -----
M101 2012-01-02  15.1  13.5
M101 2012-02-14  15.0  13.6
M101 2012-03-26  15.1  13.5
 M31 2012-01-02  17.0  17.5
 M31 2012-02-14  16.9  17.3
 M82 2012-02-14  16.2  14.5
 M82 2012-03-26  15.7  16.5